How Robust is OCR-Reasoning? Evaluating OCR-Reasoning Robustness of Vision-Language Models under Visual Perturbations
arXiv:2606.26041v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance on OCR-based benchmarks and increasingly focused on text-rich understanding, but their robustness under controlled visual degradation remains insufficiently understood. This gap is critical for OCR reasoning, where visual corruption can induce OCR errors and structural distortions, thereby introducing uncertainty into the reasoning task. To systematically study this problem, we introduce OCR-Robust, a benchmark designed for evaluating OCR reasoning robustness under visual perturbati
The rapid advancement and deployment of Vision-Language Models (VLMs) necessitate a robust understanding of their practical limitations and vulnerabilities, especially in critical applications involving text processing.
Understanding the robustness of OCR-reasoning in VLMs under visual degradation is crucial for their reliable application in real-world scenarios, particularly where data quality cannot be guaranteed.
This research introduces standardized benchmarks for evaluating VLM robustness, shifting the focus towards practical reliability over pure performance metrics in text-rich environments.
- · VLM developers focused on robustness
- · Industries reliant on accurate OCR (e.g., finance, legal)
- · Academia developing bias/robustness evaluation
- · VLM developers ignoring robustness
- · Applications deploying VLMs without thorough testing
VLMs become more reliable in processing varied quality visual text, reducing errors in automation workflows.
Increased focus on data quality preprocessing and adversarial training techniques for VLMs.
Broader adoption of VLMs in high-stakes environments where current robustness concerns are prohibitive.
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Read at arXiv cs.CL